OpenRaiser/Intern-Atlas huggingface dataset:
DeformStream: Deformation-based Adaptive Volumetric Video Streaming
Venue: arXiv cs.CV 2024
Year: 2024

Volumetric video streaming offers immersive 3D experiences but faces significant challenges due to high bandwidth requirements and latency issues in transmitting detailed content in real time. Traditional methods like point cloud streaming compromise visual quality when zoomed in, and neural rendering techniques are too computationally intensive for real-time use. Though mesh-based streaming stands out by preserving surface detail and connectivity, offering a more refined representation for 3D content, traditional mesh streaming methods typically transmit data on a per-frame basis, failing to take full advantage of temporal redundancies across frames. This results in inefficient bandwidth usage and poor adaptability to fluctuating network conditions. We introduce Deformation-based Adaptive Volumetric Video Streaming, a novel framework that enhances volumetric video streaming performance by leveraging the inherent deformability of mesh-based representations. DeformStream uses embedded deformation to reconstruct subsequent frames from inter-frame motion, significantly reducing bandwidth usage while ensuring visual coherence between frames. To address frame reconstruction overhead and network adaptability, we formulate a new QoE model that accounts for client-side deformation latency and design a dynamic programming algorithm to optimize the trade-off between visual quality and bandwidth consumption under varying network conditions. Our evaluation demonstrates that Deformation-based Adaptive Volumetric Video Streaming outperforms existing mesh-based streaming systems in both bandwidth efficiency and visual quality, offering a robust solution for real-time volumetric video applications.
Optimized Monte Carlo Tree Search for Enhanced Decision Making in the FrozenLake Environment
Venue: arXiv cs.AI 2024
Year: 2024

Monte Carlo Tree Search (MCTS) is a powerful algorithm for solving complex decision-making problems. This paper presents an optimized MCTS implementation applied to the FrozenLake environment, a classic reinforcement learning task characterized by stochastic transitions. The optimization leverages cumulative reward and visit count tables along with the Upper Confidence Bound for Trees (UCT) formula, resulting in efficient learning in a slippery grid world. We benchmark our implementation against other decision-making algorithms, including MCTS with Policy and Q-Learning, and perform a detailed comparison of their performance. The results demonstrate that our optimized approach effectively maximizes rewards and success rates while minimizing convergence time, outperforming baseline methods, especially in environments with inherent randomness.
On Your Mark, Get Set, Predict! Modeling Continuous-Time Dynamics of Cascades for Information Popularity Prediction
Venue: arXiv cs.AI 2024
Year: 2024

Information popularity prediction is important yet challenging in various domains, including viral marketing and news recommendations. The key to accurately predicting information popularity lies in subtly modeling the underlying temporal information diffusion process behind observed events of an information cascade, such as the retweets of a tweet. To this end, most existing methods either adopt recurrent networks to capture the temporal dynamics from the first to the last observed event or develop a statistical model based on self-exciting point processes to make predictions. However, information diffusion is intrinsically a complex continuous-time process with irregularly observed discrete events, which is oversimplified using recurrent networks as they fail to capture the irregular time intervals between events, or using self-exciting point processes as they lack flexibility to capture the complex diffusion process. Against this background, we propose ConCat, modeling the Continuous-time dynamics of Cascades for information popularity prediction. On the one hand, it leverages neural Ordinary Differential Equations (ODEs) to model irregular events of a cascade in continuous time based on the cascade graph and sequential event information. On the other hand, it considers cascade events as neural temporal point processes (TPPs) parameterized by a conditional intensity function which can also benefit the popularity prediction task. We conduct extensive experiments to evaluate ConCat on three real-world datasets. Results show that ConCat achieves superior performance compared to state-of-the-art baselines, yielding a 2.3%-33.2% improvement over the best-performing baselines across the three datasets.
Ascend HiFloat8 Format for Deep Learning
Venue: arXiv cs.LG 2024
Year: 2024

This preliminary white paper proposes a novel 8-bit floating-point data format HiFloat8 (abbreviated as HiF8) for deep learning. HiF8 features tapered precision. For normal value encoding, it provides 7 exponent values with 3-bit mantissa, 8 exponent values with 2-bit mantissa, and 16 exponent values with 1-bit mantissa. For denormal value encoding, it extends the dynamic range by 7 extra powers of 2, from 31 to 38 binades (notice that FP16 covers 40 binades). Meanwhile, HiF8 encodes all the special values except that positive zero and negative zero are represented by only one bit-pattern. Thanks to the better balance between precision and dynamic range, HiF8 can be simultaneously used in both forward and backward passes of AI training. In this paper, we will describe the definition and rounding methods of HiF8, as well as the tentative training and inference solutions. To demonstrate the efficacy of HiF8, massive simulation results on various neural networks, including traditional neural networks and large language models (LLMs), will also be presented.
HURRY: Highly Utilized, Reconfigurable ReRAM-based In-situ Accelerator with Multifunctionality
Venue: arXiv cs.AR 2024
Year: 2024

Resistive random-access memory (ReRAM) crossbar arrays are suitable for efficient inference computations in neural networks due to their analog general matrix-matrix multiplication (GEMM) capabilities. However, traditional ReRAM-based accelerators suffer from spatial and temporal underutilization. We present HURRY, a reconfigurable and multifunctional ReRAM-based in-situ accelerator. HURRY uses a block activation scheme for concurrent activation of dynamically sized ReRAM portions, enhancing spatial utilization. Additionally, it incorporates functional blocks for convolution, ReLU, max pooling, and softmax computations to improve temporal utilization. System-level scheduling and data mapping strategies further optimize performance. Consequently, HURRY achieves up to 3.35x speedup, 5.72x higher energy efficiency, and 7.91x greater area efficiency compared to current ReRAM-based accelerators.
Task Addition in Multi-Task Learning by Geometrical Alignment
Venue: arXiv cs.LG 2024
Year: 2024

Training deep learning models on limited data while maintaining generalization is one of the fundamental challenges in molecular property prediction. One effective solution is transferring knowledge extracted from abundant datasets to those with scarce data. Recently, a novel algorithm called Geometrically Aligned Transfer Encoder (GATE) has been introduced, which uses soft parameter sharing by aligning the geometrical shapes of task-specific latent spaces. However, GATE faces limitations in scaling to multiple tasks due to computational costs. In this study, we propose a task addition approach for GATE to improve performance on target tasks with limited data while minimizing computational complexity. It is achieved through supervised multi-task pre-training on a large dataset, followed by the addition and training of task-specific modules for each target task. Our experiments demonstrate the superior performance of the task addition strategy for GATE over conventional multi-task methods, with comparable computational costs.
Cross-Lingual and Cross-Cultural Variation in Image Descriptions
Venue: arXiv cs.CL 2024
Year: 2024

Do speakers of different languages talk differently about what they see? Behavioural and cognitive studies report cultural effects on perception; however, these are mostly limited in scope and hard to replicate. In this work, we conduct the first large-scale empirical study of cross-lingual variation in image descriptions. Using a multimodal dataset with 31 languages and images from diverse locations, we develop a method to accurately identify entities mentioned in captions and present in the images, then measure how they vary across languages. Our analysis reveals that pairs of languages that are geographically or genetically closer tend to mention the same entities more frequently. We also identify entity categories whose saliency is universally high (such as animate beings), low (clothing accessories) or displaying high variance across languages (landscape). In a case study, we measure the differences in a specific language pair (e.g., Japanese mentions clothing far more frequently than English). Furthermore, our method corroborates previous small-scale studies, including 1) Rosch et al. (1976)'s theory of basic-level categories, demonstrating a preference for entities that are neither too generic nor too specific, and 2) Miyamoto et al. (2006)'s hypothesis that environments afford patterns of perception, such as entity counts. Overall, our work reveals the presence of both universal and culture-specific patterns in entity mentions.
Pre-trained Language Models Return Distinguishable Probability Distributions to Unfaithfully Hallucinated Texts
Venue: arXiv cs.CL 2024
Year: 2024

In this work, we show the pre-trained language models return distinguishable generation probability and uncertainty distribution to unfaithfully hallucinated texts, regardless of their size and structure. By examining 24 models on 6 data sets, we find out that 88-98% of cases return statistically significantly distinguishable generation probability and uncertainty distributions. Using this general phenomenon, we showcase a hallucination-reducing training algorithm. Our algorithm outperforms other baselines by achieving higher faithfulness metrics while maintaining sound general text quality measures.
Wildlife Product Trading in Online Social Networks: A Case Study on Ivory-Related Product Sales Promotion Posts
Venue: arXiv cs.SI 2024
Year: 2024

Wildlife trafficking (WLT) has emerged as a global issue, with traffickers expanding their operations from offline to online platforms, utilizing e-commerce websites and social networks to enhance their illicit trade. This paper addresses the challenge of detecting and recognizing wildlife product sales promotion behaviors in online social networks, a crucial aspect in combating these environmentally harmful activities. To counter these environmentally damaging illegal operations, in this research, we focus on wildlife product sales promotion behaviors in online social networks. Specifically, 1) A scalable dataset related to wildlife product trading is collected using a network-based approach. This dataset is labeled through a human-in-the-loop machine learning process, distinguishing positive class samples containing wildlife product selling posts and hard-negatives representing normal posts misclassified as potential WLT posts, subsequently corrected by human annotators. 2) We benchmark the machine learning results on the proposed dataset and build a practical framework that automatically identifies suspicious wildlife selling posts and accounts, sufficiently leveraging the multi-modal nature of online social networks. 3) This research delves into an in-depth analysis of trading posts, shedding light on the systematic and organized selling behaviors prevalent in the current landscape. We provide detailed insights into the nature of these behaviors, contributing valuable information for understanding and countering illegal wildlife product trading.
CryptoTrain: Fast Secure Training on Encrypted Dataset
Venue: arXiv cs.CR 2024
Year: 2024

Secure training, while protecting the confidentiality of both data and model weights, typically incurs significant training overhead. Traditional Fully Homomorphic Encryption (FHE)-based non-inter-active training models are heavily burdened by computationally demanding bootstrapping. To develop an efficient secure training system, we established a foundational framework, CryptoTrain-B, utilizing a hybrid cryptographic protocol that merges FHE with Oblivious Transfer (OT) for handling linear and non-linear operations, respectively. This integration eliminates the need for costly bootstrapping. Although CryptoTrain-B sets a new baseline in performance, reducing its training overhead remains essential. We found that ciphertext-ciphertext multiplication (CCMul) is a critical bottleneck in operations involving encrypted inputs and models. Our solution, the CCMul-Precompute technique, involves precomputing CCMul offline and resorting to the less resource-intensive ciphertext-plaintext multiplication (CPMul) during private training. Furthermore, conventional polynomial convolution in FHE systems tends to encode irrelevant and redundant values into polynomial slots, necessitating additional polynomials and ciphertexts for input representation and leading to extra multiplications. Addressing this, we introduce correlated polynomial convolution, which encodes only related input values into polynomials, thus drastically reducing the number of computations and overheads. By integrating CCMul-Precompute and correlated polynomial convolution into CryptoTrain-B, we facilitate a rapid and efficient secure training framework, CryptoTrain. Extensive experiments demonstrate that CryptoTrain achieves a ~5.3X training time reduction compared to prior methods.
An Integrated Machine Learning and Deep Learning Framework for Credit Card Approval Prediction
Venue: arXiv cs.CE 2024
Year: 2024

Credit scoring is vital in the financial industry, assessing the risk of lending to credit card applicants. Traditional credit scoring methods face challenges with large datasets and data imbalance between creditworthy and non-creditworthy applicants. This paper introduces an advanced machine learning and deep learning framework to improve the accuracy and reliability of credit card approval predictions. We utilized extensive datasets of user application records and credit history, implementing a comprehensive preprocessing strategy, feature engineering, and model integration. Our methodology combines neural networks with an ensemble of base models, including logistic regression, support vector machines, k-nearest neighbors, decision trees, random forests, and gradient boosting. The ensemble approach addresses data imbalance using Synthetic Minority Over-sampling Technique (SMOTE) and mitigates overfitting risks. Experimental results show that our integrated model surpasses traditional single-model approaches in precision, recall, F1-score, AUC, and Kappa, providing a robust and scalable solution for credit card approval predictions. This research underscores the potential of advanced machine learning techniques to transform credit risk assessment and financial decision-making.
Skyeyes: Ground Roaming using Aerial View Images
Venue: arXiv cs.CV 2024
Year: 2024

Integrating aerial imagery-based scene generation into applications like autonomous driving and gaming enhances realism in 3D environments, but challenges remain in creating detailed content for occluded areas and ensuring real-time, consistent rendering. In this paper, we introduce Skyeyes, a novel framework that can generate photorealistic sequences of ground view images using only aerial view inputs, thereby creating a ground roaming experience. More specifically, we combine a 3D representation with a view consistent generation model, which ensures coherence between generated images. This method allows for the creation of geometrically consistent ground view images, even with large view gaps. The images maintain improved spatial-temporal coherence and realism, enhancing scene comprehension and visualization from aerial perspectives. To the best of our knowledge, there are no publicly available datasets that contain pairwise geo-aligned aerial and ground view imagery. Therefore, we build a large, synthetic, and geo-aligned dataset using Unreal Engine. Both qualitative and quantitative analyses on this synthetic dataset display superior results compared to other leading synthesis approaches. See the project page for more results: https://chaoren2357.github.io/website-skyeyes/.
CaBRNet, an open-source library for developing and evaluating Case-Based Reasoning Models
Venue: arXiv cs.AI 2024
Year: 2024

In the field of explainable AI, a vibrant effort is dedicated to the design of self-explainable models, as a more principled alternative to post-hoc methods that attempt to explain the decisions after a model opaquely makes them. However, this productive line of research suffers from common downsides: lack of reproducibility, unfeasible comparison, diverging standards. In this paper, we propose CaBRNet, an open-source, modular, backward-compatible framework for Case-Based Reasoning Networks: https://github.com/aiser-team/cabrnet.
Beyond Turing Test: Can GPT-4 Sway Experts' Decisions?
Venue: arXiv cs.CE 2024
Year: 2024

In the post-Turing era, evaluating large language models (LLMs) involves assessing generated text based on readers' reactions rather than merely its indistinguishability from human-produced content. This paper explores how LLM-generated text impacts readers' decisions, focusing on both amateur and expert audiences. Our findings indicate that GPT-4 can generate persuasive analyses affecting the decisions of both amateurs and professionals. Furthermore, we evaluate the generated text from the aspects of grammar, convincingness, logical coherence, and usefulness. The results highlight a high correlation between real-world evaluation through audience reactions and the current multi-dimensional evaluators commonly used for generative models. Overall, this paper shows the potential and risk of using generated text to sway human decisions and also points out a new direction for evaluating generated text, i.e., leveraging the reactions and decisions of readers. We release our dataset to assist future research.
PMSS: Pretrained Matrices Skeleton Selection for LLM Fine-tuning
Venue: arXiv cs.CL 2024
Year: 2024

Low-rank adaptation (LoRA) and its variants have recently gained much interest due to their ability to avoid excessive inference costs. However, LoRA still encounters the following challenges: (1) Limitation of low-rank assumption; and (2) Its initialization method may be suboptimal. To this end, we propose PMSS(Pre-trained Matrices Skeleton Selection), which enables high-rank updates with low costs while leveraging semantic and linguistic information inherent in pre-trained weight. It achieves this by selecting skeletons from the pre-trained weight matrix and only learning a small matrix instead. Experiments demonstrate that PMSS outperforms LoRA and other fine-tuning methods across tasks with much less trainable parameters. We demonstrate its effectiveness, especially in handling complex tasks such as DROP benchmark(+3.4%/+5.9% on LLaMA2-7B/13B) and math reasoning(+12.89%/+5.61%/+3.11% on LLaMA2-7B, Mistral-7B and Gemma-7B of GSM8K). The code and model will be released soon.
Non-stationary BERT: Exploring Augmented IMU Data For Robust Human Activity Recognition
Venue: arXiv cs.AI 2024
Year: 2024

Human Activity Recognition (HAR) has gained great attention from researchers due to the popularity of mobile devices and the need to observe users' daily activity data for better human-computer interaction. In this work, we collect a human activity recognition dataset called OPPOHAR consisting of phone IMU data. To facilitate the employment of HAR system in mobile phone and to achieve user-specific activity recognition, we propose a novel light-weight network called Non-stationary BERT with a two-stage training method. We also propose a simple yet effective data augmentation method to explore the deeper relationship between the accelerator and gyroscope data from the IMU. The network achieves the state-of-the-art performance testing on various activity recognition datasets and the data augmentation method demonstrates its wide applicability.
Evolutionary Greedy Algorithm for Optimal Sensor Placement Problem in Urban Sewage Surveillance
Venue: arXiv cs.CY 2024
Year: 2024

Designing a cost-effective sensor placement plan for sewage surveillance is a crucial task because it allows cost-effective early pandemic outbreak detection as supplementation for individual testing. However, this problem is computationally challenging to solve, especially for massive sewage networks having complicated topologies. In this paper, we formulate this problem as a multi-objective optimization problem to consider the conflicting objectives and put forward a novel evolutionary greedy algorithm (EG) to enable efficient and effective optimization for large-scale directed networks. The proposed model is evaluated on both small-scale synthetic networks and a large-scale, real-world sewage network in Hong Kong. The experiments on small-scale synthetic networks demonstrate a consistent efficiency improvement with reasonable optimization performance and the real-world application shows that our method is effective in generating optimal sensor placement plans to guide policy-making.
PhD Forum: Efficient Privacy-Preserving Processing via Memory-Centric Computing
Venue: arXiv cs.CR 2024
Year: 2024

Privacy-preserving computation techniques like homomorphic encryption (HE) and secure multi-party computation (SMPC) enhance data security by enabling processing on encrypted data. However, the significant computational and CPU-DRAM data movement overhead resulting from the underlying cryptographic algorithms impedes the adoption of these techniques in practice. Existing approaches focus on improving computational overhead using specialized hardware like GPUs and FPGAs, but these methods still suffer from the same processor-DRAM overhead. Novel hardware technologies that support in-memory processing have the potential to address this problem. Memory-centric computing, or processing-in-memory (PIM), brings computation closer to data by introducing low-power processors called data processing units (DPUs) into memory. Besides its in-memory computation capability, PIM provides extensive parallelism, resulting in significant performance improvement over state-of-the-art approaches. We propose a framework that uses recently available PIM hardware to achieve efficient privacy-preserving computation. Our design consists of a four-layer architecture: (1) an application layer that decouples privacy-preserving applications from the underlying protocols and hardware; (2) a protocol layer that implements existing secure computation protocols (HE and MPC); (3) a data orchestration layer that leverages data compression techniques to mitigate the data transfer overhead between DPUs and host memory; (4) a computation layer which implements DPU kernels on which secure computation algorithms are built.